Medical reports contain a great deal of information that characterizes a patient's medical condition. A large percent of this information, however, is unstructured in the form of free text. In this form, the information is difficult to search, sort, analyze, summarize and present. We present a new method of natural language processing which attempts to automatically extract the important concepts from medical free text reports. The sentence parsing algorithm is based on a "field theory" that is motivated by how a multi-particle system forms attachments as explained in physics. This view of natural language processing attempts to view words as active entities rather than passive data elements. A model for how and why word attachments occur is presented. The model presents explanations in terms of both energy fields that characterize the stability of a word at a given state of the sentence parse and in terms of a signal processing model involving a resonance phenomenon. Our hypotheses are that: 1) the system can extract information from medical reports; 2) the results can be represented in a canonical form with respect to meaning; 3) the system is extendable to many domains of medicine. The testing and evaluation of our system will be performed on reports from the specific domains of radiology. We will explore the adaptability of the NLP system to the domains of pediatric pathology and urology. In addition to technical measures, NLP will be evaluated from the end-user perspective including the selection of appropriate imaging protocols, SNOMED coding, and information retrieval from radiology teaching cases.